Title

1 Data

acc1_cancer_cells = readRDS("./Data/acc1_cancer_cells_15KnCount_V3.RDS")
all_acc_cancer_cells = readRDS("./Data/acc_cancer_cells_V4.RDS")
acc_all_cells = readRDS("./Data/acc_tpm_nCount_mito_no146_15k_with_ACC1_.RDS")
acc_cancerCells_noACC1 = readRDS("./Data/acc_cancer_no146_primaryonly15k_cancercells.rds")

luminal_pathways = c("CHARAFE_BREAST_CANCER_LUMINAL_VS_BASAL_DN","CHARAFE_BREAST_CANCER_LUMINAL_VS_BASAL_UP","CHARAFE_BREAST_CANCER_LUMINAL_VS_MESENCHYMAL_DN","CHARAFE_BREAST_CANCER_LUMINAL_VS_MESENCHYMAL_UP","HUPER_BREAST_BASAL_VS_LUMINAL_DN","LIM_MAMMARY_LUMINAL_PROGENITOR_UP","SMID_BREAST_CANCER_LUMINAL_B_UP" )

# add luminal pathwaysx 
luminal_gs = msigdbr(species = "Homo sapiens") %>%as.data.frame() %>% dplyr::filter(gs_name %in% luminal_pathways)%>% dplyr::distinct(gs_name, gene_symbol) %>% as.data.frame()

2 Parameters

3 functions

source_from_github(repositoy = "DEG_functions",version = "0.2.24")
source_from_github(repositoy = "HMSC_functions",version = "0.1.13",script_name = "functions.R")
source_from_github(repositoy = "cNMF_functions",version = "0.3.72",script_name = "cnmf_function_Harmony.R")

4 UMAP

DimPlot(object = acc1_cancer_cells,pt.size = 2)

deg = FindAllMarkers(object = acc1_cancer_cells,features = VariableFeatures(acc1_cancer_cells),densify = T)

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for (cluster in unique(deg$cluster)) {
  print(deg[deg$cluster == cluster,])
}

for (cluster in unique(deg$cluster)) {
  deg_of_cluster = deg[deg$cluster == cluster,]
  enrichment_analysis(deg_of_cluster,background =VariableFeatures(acc1_cancer_cells),fdr_Cutoff = 0.01,ident.1 = paste("Cluster",cluster),ident.2 =  paste("Cluster",cluster),show_by = 1)
}

NA
NA

4.1 features UMAP

FeaturePlot(object = acc1_cancer_cells,features = c("TP63","ACTA2","IL12B","CNN1"))

Warning in FeaturePlot(object = acc1_cancer_cells, features = c("TP63",  :
  All cells have the same value (0) of IL12B.

5 Enrichment analysis HMSC vs ACC

all_acc_cancer_cells = SetIdent(all_acc_cancer_cells, value ="patient.ident")
acc_deg <- FindMarkers(all_acc_cancer_cells, ident.1 = "HMSC",logfc.threshold = 1.5,features = VariableFeatures(all_acc_cancer_cells))
enrichment_analysis(acc_deg,background = VariableFeatures(all_acc_cancer_cells),fdr_Cutoff = 0.01,ident.1 = "HMSC",ident.2 = "ACC",show_by = 1)

5.1 cell cycle filtering

hallmark_name = "GO_MITOTIC_CELL_CYCLE"
genesets  =getGmt("./Data/h.all.v7.0.symbols.pluscc.gmt")
var_features=acc_cancerCells_noACC1@assays$RNA@var.features
geneIds= genesets[[hallmark_name]]@geneIds
score <- apply(acc_cancerCells_noACC1@assays$RNA@scale.data[intersect(geneIds,var_features),],2,mean)
acc_cancerCells_noACC1=AddMetaData(acc_cancerCells_noACC1,score,hallmark_name)

hallmark_name = "GO_MITOTIC_CELL_CYCLE"
genesets  =getGmt("./Data/h.all.v7.0.symbols.pluscc.gmt")
var_features=acc1_cancer_cells@assays$RNA@var.features
geneIds= genesets[[hallmark_name]]@geneIds
score <- apply(acc1_cancer_cells@assays$RNA@scale.data[intersect(geneIds,var_features),],2,mean)
acc1_cancer_cells=AddMetaData(acc1_cancer_cells,score,hallmark_name)


# hallmark_name = "GO_MITOTIC_CELL_CYCLE"
# genesets  =getGmt("./Data/h.all.v7.0.symbols.pluscc.gmt")
# var_features=acc1_cancer_cells@assays$RNA@var.features
# geneIds= genesets[[hallmark_name]]@geneIds
# score <- apply(acc1_cancer_cells@assays$RNA@scale.data[intersect(geneIds,var_features),],2,mean)
# acc1_cancer_cells=AddMetaData(acc1_cancer_cells,score,hallmark_name)

library(highcharter) 
options(highcharter.theme = hc_theme_smpl(tooltip = list(valueDecimals = 2)))

cc_scores = FetchData(object = acc1_cancer_cells,vars = "GO_MITOTIC_CELL_CYCLE")
 hchart(
  density(cc_scores$GO_MITOTIC_CELL_CYCLE), 
  type = "area", name = "GO_MITOTIC_CELL_CYCLE"
  )

NA

cc_scores = FetchData(object = acc_cancerCells_noACC1,vars = "GO_MITOTIC_CELL_CYCLE")
 hchart(
  density(cc_scores$GO_MITOTIC_CELL_CYCLE), 
  type = "area", name = "GO_MITOTIC_CELL_CYCLE"
  )

NA

acc_cc_scores = FetchData(object = acc_cancerCells_noACC1,vars = "GO_MITOTIC_CELL_CYCLE")
hmsc_cc_scores = FetchData(object = acc1_cancer_cells,vars = "GO_MITOTIC_CELL_CYCLE")

ggplot() +
  geom_density(aes(GO_MITOTIC_CELL_CYCLE, fill = "ACC"), alpha = .2, data = acc_cc_scores) +
  geom_density(aes(GO_MITOTIC_CELL_CYCLE, fill = "HMSC"), alpha = .2, data = hmsc_cc_scores) +
  scale_fill_manual(name = "Dataset", values = c(ACC = "red", HMSC = "green"))+ geom_vline(aes(xintercept=0.15),
            color="blue", linetype="dashed", size=1)

5.1.1 Before cc filtering

#filter:
all_acc_cancer_cells_ccFiltered=all_acc_cancer_cells[,all_acc_cancer_cells@meta.data[[hallmark_name]]< 0.15]


min_threshold = min(all_acc_cancer_cells$GO_MITOTIC_CELL_CYCLE)
max_threshold = max(all_acc_cancer_cells$GO_MITOTIC_CELL_CYCLE)

library(viridis)

Loading required package: viridisLite

FeaturePlot(object = all_acc_cancer_cells,features = hallmark_name) + ggtitle("Before cc filtering")  & scale_color_gradientn(colours = plasma(n = 10, direction = -1), limits = c(min_threshold, max_threshold))

Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.
Warning in grSoftVersion() :
  unable to load shared object '/usr/local/lib/R/modules//R_X11.so':
  libXt.so.6: cannot open shared object file: No such file or directory

5.1.2 After cc filtering

FeaturePlot(object = all_acc_cancer_cells_ccFiltered,features = hallmark_name) + ggtitle("After cc filtering") & scale_color_gradientn(colours = plasma(n = 10, direction = -1), limits = c(min_threshold, max_threshold))

Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the existing scale.

5.2 Enrichment analysis filtered HMSC vs ACC

1
patient.ident = all_acc_cancer_cells_ccFiltered$patient.ident %>% as.data.frame()
patient.ident[,1] = as.character(patient.ident[,1])
patient.ident[patient.ident[,1] == "ACC1",] = "HMSC"
patient.ident[,1] = as.factor(patient.ident[,1])
all_acc_cancer_cells_ccFiltered = AddMetaData(object = all_acc_cancer_cells_ccFiltered,metadata = patient.ident,col.name = "patient.ident")
all_acc_cancer_cells_ccFiltered = SetIdent(all_acc_cancer_cells_ccFiltered, value ="patient.ident")
acc_deg <- FindMarkers(all_acc_cancer_cells_ccFiltered, ident.1 = "HMSC",logfc.threshold = 1.5,features = VariableFeatures(all_acc_cancer_cells_ccFiltered))
enrichment_analysis(acc_deg,background = VariableFeatures(all_acc_cancer_cells_ccFiltered),fdr_Cutoff = 0.01,ident.1 = "HMSC",ident.2 = "ACC",show_by = 1)

6 MYB expression

all_acc_cancer_cells = SetIdent(object = all_acc_cancer_cells,value = "patient.ident") #active snn graph
FeaturePlot(object = all_acc_cancer_cells,features = "MYB",label = T)

all_acc_cancer_cells = SetIdent(object = all_acc_cancer_cells,value = "patient.ident") #active snn graph
FeaturePlot(object = all_acc_cancer_cells,features = "MYB",label = T)

7 CNV

new.cluster.ids <- c("cancer", #0
                     "cancer", #1
                     "CAF", #2
                     "cancer", #3
                     "Endothelial", #4
                     "cancer", #5
                     "cancer", #6
                     "CAF", #7
                     "CAF", #8
                     "CAF", #9
                     "cancer", #10
                     "CAF", #11
                     "cancer", #12
                     "cancer", #13
                     "cancer", #14
                     "cancer", #15
                     "cancer", #16
                     "WBC", #17
                     "CAF" #18
                     )

#rename idents:
acc_all_cells = SetIdent(object = acc_all_cells,value = "RNA_snn_res.1") #active snn graph
names(new.cluster.ids) <- levels(acc_all_cells) #add snn graph levels to new.cluster.ids
acc_all_cells@meta.data[["seurat_clusters"]] = acc_all_cells@meta.data[["RNA_snn_res.1"]]
acc_all_cells = SetIdent(object = acc_all_cells,value = "seurat_clusters")
acc_all_cells <- RenameIdents(acc_all_cells, new.cluster.ids) 

# divide "cancer" into patients:
cell_types = acc_all_cells@active.ident %>% as.data.frame()
cell_types[,1]<- as.character(cell_types[,1])
cell_types = cbind(cell_types,acc_all_cells$patient.ident) %>% setnames(old = names(.), 
         new = c('cell_type','patient'))
cell_types[cell_types$cell_type == "cancer",] = cell_types[cell_types$cell_type == "cancer",2]


# hmsc_rows = (startsWith(x = rownames(cell_types),prefix = "ACC.plate2") | startsWith(x = rownames(cell_types),prefix = "ACC1.")) & cell_types[,1] == "cancer" 
# acc_rows = !(startsWith(x = rownames(cell_types),prefix = "ACC.plate2") | startsWith(x = rownames(cell_types),prefix = "ACC1.")) & cell_types[,1] == "cancer" 
# cell_types[,1][hmsc_rows]  = "HMSC"
# cell_types[,1][acc_rows]  = "ACC"

#add to metadata:
cell_types[,2] = NULL 
cell_types[cell_types$cell_type == "ACC1",] = "HMSC"
acc_all_cells = AddMetaData(object =acc_all_cells ,metadata = cell_types,col.name = "cell.type")

7.1 CNV UMAP

library(infercnv)
library(tidyverse)
acc_annotation  = as.data.frame(acc_all_cells@meta.data[,"cell.type",drop = F])
acc_annotation = acc_annotation %>% rownames_to_column("orig.ident") 
acc_annotation = acc_annotation %>% mutate(orig.ident = gsub(x = acc_annotation$orig.ident,pattern = "\\.", replacement = "-") %>% 
  gsub(pattern = "_", replacement = "-"))
  

write.table(acc_annotation, "./Data/inferCNV/acc_annotation.txt", append = FALSE, 
            sep = "\t", dec = ".",row.names = FALSE, col.names = F)

infercnv_obj = CreateInfercnvObject(raw_counts_matrix="./Data/inferCNV/all.4icnv.txt", 
                                    annotations_file="./Data/inferCNV/acc_annotation.txt",
                                    delim="\t",gene_order_file="./Data/inferCNV/gencode_v19_gene_pos.txt"
                                    ,ref_group_names=c("CAF", "Endothelial", "WBC")) #groups of normal cells

infercnv_obj_default = infercnv::run(infercnv_obj, cutoff=1, out_dir='./Data/inferCNV/infercnv_output',
                                     cluster_by_groups=T, plot_steps=FALSE,
                                     denoise=TRUE, HMM=FALSE, no_prelim_plot=TRUE,
                                     png_res=300)
plot_cnv(infercnv_obj_default, output_format = "png",  write_expr_matrix = FALSE,out_dir = "./Data/inferCNV/",png_res   =800,obs_title = "Malignant cells",ref_title = "Normal cells")


cluster.info=FetchData(acc_all_cells,c("ident","orig.ident","UMAP_1","UMAP_2","nCount_RNA","nFeature_RNA","percent.mt","patient.ident","seurat_clusters"))
cluster.info$cell=rownames(cluster.info)

library(limma)
smoothed=apply(infercnv_obj_default@expr.data,2,tricubeMovingAverage, span=0.01)
cnsig=sqrt(apply((smoothed-1)^2,2,mean))
umap=cluster.info
names(cnsig)=umap$cell

acc_all_cells <- AddMetaData(object = acc_all_cells, metadata = cnsig, col.name = "copynumber")
acc_all_cells = SetIdent(object = acc_all_cells,value = "cell.type")
FeaturePlot(acc_all_cells, "copynumber",pt.size = 1, cols = c("lightblue","orange","red","darkred"),label = T,repel = T)

acc_cancer_cells <- AddMetaData(object = acc_cancer_cells, metadata = cnsig, col.name = "copynumber")
acc_cancer_cells = SetIdent(object = acc_cancer_cells,value = "cell.type")
FeaturePlot(acc_cancer_cells, "copynumber",pt.size = 1, cols = c("lightblue","orange","red","darkred"),label = T,repel = T)

7.2 CNV plot

CNV plot

7.3 CNV subtypes

cnv_subtypes = as.data.frame(cutree(infercnv_obj_default@tumor_subclusters[["hc"]][["HMSC"]], k = 2))
names(cnv_subtypes)[1] = "cnv.cluster"
rownames(cnv_subtypes) = rownames(cnv_subtypes) %>% gsub(pattern = "-",replacement = "\\.")
cnv_subtypes [,1] = as.character(cnv_subtypes[,1])
infercnv.observations = data.frame(fread(file = "./Data/inferCNV/infercnv.observations.txt"), row.names=1)
names_to_keep = colnames(infercnv.observations) %in% (colnames(acc1_cancer_cells) %>% gsub(pattern = "_",replacement = "\\."))
infercnv.observations = infercnv.observations[,names_to_keep]

rownames(cnv_subtypes) = rownames(cnv_subtypes) %>% gsub(pattern = "2\\.",replacement = "2_")
rownames(cnv_subtypes) = rownames(cnv_subtypes) %>% gsub(pattern = "3\\.",replacement = "3_")
acc1_cancer_cells = AddMetaData(object = acc1_cancer_cells,metadata = cnv_subtypes)

DimPlot(acc1_cancer_cells,group.by = "cnv.cluster",pt.size = 2)

8 Myo-Lum score

8.1 Original score

original_myo_genes = c( "TP63", "TP73", "CAV1", "CDH3", "KRT5", "KRT14", "ACTA2", "TAGLN", "MYLK", "DKK3")
original_lum_genes = c("KIT", "EHF", "ELF5", "KRT7", "CLDN3", "CLDN4", "CD24", "LGALS3", "LCN2", "SLPI" )

calculate_score(dataset = all_acc_cancer_cells,myo_genes = original_myo_genes,lum_genes = original_lum_genes)

correlation of lum score and myo score: -0.45
correlation of lum score and original lum score: 1
correlation of myo score and original myo score: 1

8.2 Original score of ACC1

calculate_score(dataset = acc1_cancer_cells,myo_genes = original_myo_genes,lum_genes = original_lum_genes,lum_threshold = 0,myo_threshold = 0)

Warning: Could not find TP73 in the default search locations, found in RNA assay instead
Warning: Could not find MYLK in the default search locations, found in RNA assay instead
Warning: Could not find CLDN3 in the default search locations, found in RNA assay instead
Warning: Could not find CD24 in the default search locations, found in RNA assay instead
correlation of lum score and myo score: -0.11

8.3 0.35 Most correlated score

8.3.1 Myo genes

myo_protein_markers = c("CNN1", "TP63","ACTA2")
top_myo  = top_correlated(dataset = acc1_cancer_cells, genes = myo_protein_markers,threshold = 0.35)
print("Number of genes = " %>% paste(length(top_myo)))

[1] "Number of genes =  20"

message("Names of genes:")

Names of genes:

top_myo %>% head(30)

 [1] "COL16A1"     "RP1-39G22.4" "ACTG2"       "CD200"       "MYLK"        "TP63"        "KCNMB1"      "ADAMTS2"     "LOXL2"      
[10] "TPM2"        "CLIC3"       "SNCG"        "ACTA2"       "TAGLN"       "A2M"         "NGFR"        "CNN1"        "PPP1R14A"   
[19] "MYL9"        "POM121L9P"  

message("Genes that also apeared in the original score:")

Genes that also apeared in the original score:

base::intersect(top_myo,original_myo_genes) 

[1] "MYLK"  "TP63"  "ACTA2" "TAGLN"

myo_enrich_res = genes_vec_enrichment(genes = top_myo,background = rownames(acc1_cancer_cells),homer = T,title = "myo top enrichment",custom_pathways = luminal_gs)

myo_enrich_res

8.3.2 Lum genes

lum_protein_markers = c("KIT")
top_lum  = top_correlated(dataset = acc1_cancer_cells, genes = lum_protein_markers,threshold = 0.35,n_vargenes = 5000)
print("Number of genes = " %>% paste(length(top_lum)))

[1] "Number of genes =  5"

message("Names of genes:")

Names of genes:

top_lum %>% head(30)

[1] "B3GNT2"  "GLRB"    "EFNA5"   "ALDH3B2" "CCND1"  

message("Genes that also apeared in the original score:")

Genes that also apeared in the original score:

base::intersect(top_lum,original_lum_genes) 

character(0)

lum_enrich_res = genes_vec_enrichment(genes = top_lum,background = rownames(acc1_cancer_cells),homer = T,title = "lum top enrichment",custom_pathways = luminal_gs)

lum_enrich_res

8.3.3 top correlated score

calculate_score(dataset = acc1_cancer_cells,myo_genes = top_myo,lum_genes = top_lum,lum_threshold = 0,myo_threshold = 0)

correlation of lum score and myo score: -0.05
correlation of lum score and original lum score: 0.58
correlation of myo score and original myo score: 0.77

8.3.4 enriched genes score

rownames(lum_enrich_res) = lum_enrich_res$pathway_name
lum_enriched_genes = lum_enrich_res[1,"geneID"] %>% strsplit(split = "/") %>% .[[1]] %>% c(.,lum_protein_markers) #add original markers

rownames(myo_enrich_res) = myo_enrich_res$pathway_name
myo_enriched_genes = myo_enrich_res[1,"geneID"] %>% strsplit(split = "/") %>% .[[1]] %>% c(.,myo_protein_markers) #add original markers

calculate_score(dataset = acc1_cancer_cells,myo_genes = myo_enriched_genes,lum_genes = lum_enriched_genes,lum_threshold = -2.5,myo_threshold = -2.5)

correlation of lum score and myo score: -0.16
correlation of lum score and original lum score: 0.43
correlation of myo score and original myo score: 0.79

8.4 0.2 Most correlated score

8.4.1 myo Genes

n_vargenes = 2000
myo_protein_markers = c("CNN1", "TP63","ACTA2")
top_myo  = top_correlated(dataset = acc1_cancer_cells, genes = myo_protein_markers,threshold = 0.2,n_vargenes = n_vargenes)
print("Number of genes = " %>% paste(length(top_myo)))

[1] "Number of genes =  48"

message("Names of genes:")

Names of genes:

top_myo %>% head(30)

 [1] "PLOD1"         "RP1-39G22.4"   "PDZK1"         "CSRP1"         "CHI3L1"        "HIST3H3"      
 [7] "AC104699.1"    "ACTG2"         "LYG1"          "DALRD3"        "CD200"         "RP11-627C21.1"
[13] "FGFBP2"        "IGFBP7"        "HSPB3"         "RP11-168A11.4" "SPARC"         "CD83"         
[19] "HLA-DOB"       "TBCC"          "NFKBIE"        "MIR3662"       "SMOC2"         "FBXL6"        
[25] "TPM2"          "SNCG"          "ACTA2"         "DKK3"          "MIR7113"       "CTA-797E19.3" 

message("Genes that also apeared in the original score:")

Genes that also apeared in the original score:

base::intersect(top_myo,original_myo_genes) 

[1] "ACTA2" "DKK3"  "TAGLN"

myo_enrich_res = genes_vec_enrichment(genes = top_myo,background = VariableFeatures(acc1_cancer_cells) %>% head(n_vargenes),homer = T,title = "myo top enrichment",custom_pathways = luminal_gs)

myo_enrich_res

8.4.2 Lum Genes

acc1_cancer_cells = FindVariableFeatures(object = acc1_cancer_cells,nfeatures = 15000)

Calculating gene variances
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|

lum_protein_markers = c("KIT")
n_vargenes = 2000
top_lum  = top_correlated(dataset = acc1_cancer_cells, genes = lum_protein_markers,threshold = 0.3,n_vargenes = n_vargenes)
print("Number of genes = " %>% paste(length(top_lum)))

[1] "Number of genes =  13"

message("Names of genes:")

Names of genes:

top_lum %>% head(30)

 [1] "CSDE1"   "RRP9"    "TKT"     "TFG"     "ATP1B3"  "EFNA5"   "GABRP"   "CALML5"  "MMP7"    "SNORD68"
[11] "APP"     "SDF2L1"  "SAT1"   

message("Genes that also apeared in the original score:")

Genes that also apeared in the original score:

base::intersect(top_lum,original_lum_genes) 

character(0)

lum_enrich_res = genes_vec_enrichment(genes = top_lum,background = VariableFeatures(acc1_cancer_cells) %>% head(n_vargenes),homer = T,title = "lum top enrichment",custom_pathways = luminal_gs)

lum_enrich_res

calculate_score(dataset = acc1_cancer_cells,myo_genes = top_myo,lum_genes = top_lum,lum_threshold = 2,myo_threshold = 1)

correlation of lum score and myo score: -0.02
correlation of lum score and original lum score: 0.65
correlation of myo score and original myo score: 0.77

myo_intersected = intersect(top_myo,original_myo_genes) 
lum_intersected = intersect(top_lum,original_lum_genes) 
message("genes in myo score:")

genes in myo score:

myo_intersected

[1] "ACTA2" "DKK3"  "TAGLN"

message("genes in lum score:")

genes in lum score:

lum_intersected

[1] "LGALS3"

calculate_score(dataset = acc1_cancer_cells,myo_genes = myo_intersected,lum_genes = lum_intersected,lum_threshold = 2,myo_threshold = 1)

correlation of lum score and myo score: 0.03
correlation of lum score and original lum score: 0.69
correlation of myo score and original myo score: 0.81

8.4.3 enriched genes

rownames(lum_enrich_res) = lum_enrich_res$pathway_name
lum_enriched_genes = lum_enrich_res[3,"geneID"] %>% strsplit(split = "/") %>% .[[1]] %>% c(.,lum_protein_markers) #add original markers

rownames(myo_enrich_res) = myo_enrich_res$pathway_name
myo_enriched_genes = myo_enrich_res[3,"geneID"] %>% strsplit(split = "/") %>% .[[1]] %>% c(.,myo_protein_markers) #add original markers

message("genes in myo score:")

genes in myo score:

myo_enriched_genes

 [1] "FBLIM1"  "NEXN"    "NMNAT2"  "MYLK"    "CCDC50"  "IGFBP7"  "RAI14"   "ARAP3"   "SPARC"   "CALD1"   "LOXL2"  
[12] "COL5A1"  "ACTA2"   "DKK3"    "MSRB3"   "COL4A1"  "ACTN1"   "TPM1"    "TGFB1I1" "ADORA2B" "MXRA7"   "CNN1"   
[23] "TP63"    "ACTA2"  

message("genes in lum score:")

genes in lum score:

lum_enriched_genes

 [1] "PADI2"      "PATJ"       "PEX11B"     "APH1A"      "C1orf43"    "EFNA4"      "NECTIN4"    "SCYL3"      "ELF3"      
[10] "SOX13"      "IRF6"       "MED28"      "EPB41L4A"   "RGL2"       "C6orf132"   "TPD52L1"    "ICA1"       "MACC1"     
[19] "TRPS1"      "FAM83H"     "RASEF"      "ARRDC1"     "COMMD3"     "ANK3"       "GSTO2"      "PDCD4"      "EHF"       
[28] "ALDH3B2"    "SHANK2"     "SORL1"      "FKBP4"      "PTPN6"      "DUSP16"     "RERG"       "ADCY6"      "ERBB3"     
[37] "ERP29"      "SUSD6"      "RPS6KA5"    "SPINT1"     "FEM1B"      "TLE3"       "SCAMP2"     "CLN3"       "ADGRG1"    
[46] "ATP2C2"     "GGT6"       "MYO1D"      "ST6GALNAC2" "CYB5A"      "BLVRB"      "VRK3"       "SYCP2"      "TMPRSS2"   
[55] "LIMK2"      "KIT"       

calculate_score(dataset = acc1_cancer_cells,myo_genes = myo_enriched_genes,lum_genes = lum_enriched_genes,lum_threshold = 0,myo_threshold = -1)

correlation of lum score and myo score: 0.1
correlation of lum score and original lum score: 0.71
correlation of myo score and original myo score: 0.76

8.4.4 enriched genes and in original score

myo_enriched_genes = myo_enriched_genes[myo_enriched_genes %in% original_myo_genes]
lum_enriched_genes = lum_enriched_genes[lum_enriched_genes %in% original_lum_genes]

message("genes in myo score:")

genes in myo score:

myo_enriched_genes

[1] "MYLK"  "ACTA2" "DKK3"  "TP63"  "ACTA2"

message("genes in lum score:")

genes in lum score:

lum_enriched_genes

[1] "EHF" "KIT"

calculate_score(dataset = acc1_cancer_cells,myo_genes = myo_enriched_genes,lum_genes = lum_enriched_genes,lum_threshold = 2,myo_threshold = 2)

correlation of lum score and myo score: -0.07
correlation of lum score and original lum score: 0.62
correlation of myo score and original myo score: 0.77

9 HPV

Only HMSC cancer cells:

HPV33_P3 = fread("./Data/HPV33_P3.txt",col.names = c("plate","reads")) %>% as.data.frame()
HPV33_P3.df = HPV33_P3 %>% mutate(
  plate = gsub(x =HPV33_P3$plate, replacement = "",pattern = "_.*$") 
  %>% gsub(pattern = "-P",replacement = ".P") 
  %>% gsub(pattern = "-",replacement = "_",)
  )
HPV33_P3.df = HPV33_P3.df %>% dplyr::filter(HPV33_P3.df$plate %in% colnames(acc1_cancer_cells))
rownames(HPV33_P3.df)  <- HPV33_P3.df$plate
HPV33_P3.df$plate = NULL


HPV33_P2 = fread("./Data/HPV33_P2.txt",col.names = c("plate","reads")) %>% as.data.frame()
HPV33_P2.df = HPV33_P2 %>% mutate(
  plate = gsub(x =HPV33_P2$plate, replacement = "",pattern = "_.*$") 
  %>% gsub(pattern = "plate2-",replacement = "plate2_",)
  %>% gsub(pattern = "-",replacement = "\\.",)
  )
HPV33_P2.df = HPV33_P2.df %>% dplyr::filter(HPV33_P2.df$plate %in% colnames(acc1_cancer_cells))
rownames(HPV33_P2.df)  <- HPV33_P2.df$plate
HPV33_P2.df$plate = NULL

HPV33 = rbind(HPV33_P3.df,HPV33_P2.df)
acc1_cancer_cells = AddMetaData(object = acc1_cancer_cells,metadata = HPV33,col.name = "HPV33.reads")
FeaturePlot(acc1_cancer_cells,features = "HPV33.reads",max.cutoff = 10)

data = FetchData(object = acc1_cancer_cells,vars = "HPV33.reads")

data = data %>% mutate("0 reads" = if_else(condition = HPV33.reads == 0,true = 1,false = 0))
data = data %>% mutate("1 reads" = if_else(condition = HPV33.reads == 1,true = 1,false = 0))
data = data %>% mutate("2 reads" = if_else(condition = HPV33.reads == 2,true = 1,false = 0))
data = data %>% mutate("3-23 reads" = if_else(condition = HPV33.reads >=3 &HPV33.reads  <24,true = 1,false = 0))
data = data %>% mutate("24+ reads" = if_else(condition = HPV33.reads >=24,true = 1,false = 0))
data = colSums(data[,2:ncol(data)]) %>% as.data.frame()
names(data)[1] = "count"
data = rownames_to_column(data,var = "bin")
data

ggplot(data=data, aes(x=factor(bin,levels = c("0 reads","1 reads","2 reads","3-23 reads","24+ reads")), y=count)) +
  geom_bar(stat="identity", fill="steelblue") + xlab("HPV Reads")+ theme_minimal()+
  geom_text(aes(label=count), vjust=-0.5, color="black", size=3.5)

hpv33_positive = HPV33 %>% dplyr::mutate(hpv33_positive = case_when(reads >= 10 ~ "positive",
                                                                    reads < 10 ~ "negative")
)



hpv33_positive$reads = NULL
acc1_cancer_cells = AddMetaData(object = acc1_cancer_cells,metadata = hpv33_positive)

DimPlot(object = acc1_cancer_cells,group.by  = c("hpv33_positive"),pt.size = 2)

acc1_cancer_cells$plate = acc1_cancer_cells$orig.ident
saveRDS(object = acc1_cancer_cells, file = "./Data/acc1_cancer_cells_15KnCount_V4.RDS")

10 cNMF

library(reticulate)

acc1_cancer_cells = FindVariableFeatures(object = acc1_cancer_cells,nfeatures = nfeatures)

Calculating gene variances
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|

from cnmf import cNMF
import numpy as np
nfeatures_name = r.nfeatures_name
name = 'HMSC_cNMF_'+nfeatures_name+'Kvargenes'
outdir = './Data/cNMF'
K_range = np.arange(3,10)
cnmf_obj = cNMF(output_dir=outdir, name=name)
counts_fn='./Data/cNMF/hmsc_expressionData_'+nfeatures_name+'Kvargenes.txt'
tpm_fn = counts_fn ## This is a weird case where because this dataset is not 3' end umi sequencing, we opted to use the TPM matrix as the input matrix rather than the count matrix

cnmf_obj.prepare(counts_fn=counts_fn, components=K_range, seed=14,tpm_fn=tpm_fn)

cnmf_obj.factorize(worker_i=0, total_workers=1)

cnmf_obj.combine()
cnmf_obj.k_selection_plot()

10.1 Save object

# import pickle
# f = open('./Data/cNMF/HMSC_cNMF_'+nfeatures_name+'Kvargenes/cnmf_obj.pckl', 'wb')
# pickle.dump(cnmf_obj, f)
# f.close()

10.2 Load object

from cnmf import cNMF
import pickle
nfeatures = "2K"
f = open('./Data/cNMF/HMSC_cNMF_' + nfeatures+ 'vargenes/cnmf_obj.pckl', 'rb')
cnmf_obj = pickle.load(f)
f.close()

selected_k = 4
density_threshold = 0.1
cnmf_obj.consensus(k=selected_k, density_threshold=density_threshold,show_clustering=True)

/sci/labs/yotamd/lab_share/avishai.wizel/python_envs/miniconda/envs/cnmf_env_6/lib/python3.7/site-packages/scanpy/preprocessing/_simple.py:843: UserWarning: Received a view of an AnnData. Making a copy.
  view_to_actual(adata)

usage_norm, gep_scores, gep_tpm, topgenes = cnmf_obj.load_results(K=selected_k, density_threshold=density_threshold)

gep_scores = py$gep_scores
gep_tpm = py$gep_tpm
all_metagenes= py$usage_norm

10.3 Enrichment analysis by top 200 genes of each program

plt_list = list()
for (i in 1:ncol(gep_scores)) {
  top_genes = gep_scores  %>%  arrange(desc(gep_scores[i])) #sort by score a
  top = head(rownames(top_genes),200) #take top top_genes_num
  res = genes_vec_enrichment(genes = top,background = rownames(gep_scores),homer = T,title = 
                    i,silent = T,return_all = T)
   
  plt_list[[i]] = res$plt
}
gridExtra::grid.arrange(grobs = plt_list)

canonical_pathways = msigdbr(species = "Homo sapiens", category = "C2") %>% dplyr::filter(gs_subcat != "CGP") %>%  dplyr::distinct(gs_name, gene_symbol) 

plt_list = list()
for (i in 1:ncol(gep_scores)) {
  top_genes = gep_scores  %>%  arrange(desc(gep_scores[i])) #sort by score a
  top = head(rownames(top_genes),200) #take top top_genes_num
  res = genes_vec_enrichment(genes = top,background = rownames(gep_scores),homer = T,title = 
                    i,silent = T,return_all = T,custom_pathways = canonical_pathways)
   
  plt_list[[i]] = res$plt
}
gridExtra::grid.arrange(grobs = plt_list)

# lum genes in metagenes
message("lum genes in metagenes")

lum genes in metagenes

for (i in 1:ncol(gep_scores)) {
  top_genes = gep_scores  %>%  arrange(desc(gep_scores[i])) #sort by score a
  top = head(rownames(top_genes),200) #take top top_genes_num
  cat(paste0("metagene ",i,": "))
  print(original_lum_genes[original_lum_genes %in% top])

}

metagene 1: [1] "KRT7" "SLPI"
metagene 2: [1] "CLDN4"
metagene 3: character(0)
metagene 4: [1] "KRT7"   "LGALS3" "LCN2"   "SLPI"  

cat("\n")

# myo genes in metagenes
message("myo genes in metagenes")

myo genes in metagenes

for (i in 1:ncol(gep_scores)) {
  top_genes = gep_scores  %>%  arrange(desc(gep_scores[i])) #sort by score a
  top = head(rownames(top_genes),200) #take top top_genes_num
  cat(paste0("metagene ",i,": "))
  print(original_myo_genes[original_myo_genes %in% top])

}

metagene 1: [1] "KRT14" "ACTA2" "DKK3" 
metagene 2: character(0)
metagene 3: character(0)
metagene 4: character(0)

cat("\n")

notch_genes = c("JAG1","JAG2","NOTCH3","NOTCH2","NOTCH1","DLL1","MYB","HES4","HEY1","HEY2","NRARP")
# notch genes in metagenes
message("myo genes in metagenes")

myo genes in metagenes

for (i in 1:ncol(gep_scores)) {
  top_genes = gep_scores  %>%  arrange(desc(gep_scores[i])) #sort by score a
  top = head(rownames(top_genes),200) #take top top_genes_num
  cat(paste0("metagene ",i,": "))
  print(notch_genes[notch_genes %in% top])

}

metagene 1: character(0)
metagene 2: character(0)
metagene 3: character(0)
metagene 4: character(0)

cat("\n")

plt_list = list()
for (i in 1:ncol(gep_scores)) {
  top_genes = gep_scores  %>%  arrange(desc(gep_scores[i])) #sort by score a
  top = head(rownames(top_genes),10) #take top top_genes_num
  message(paste("program ",i,"top genes:"))
  print(top)

}

program  1 top genes:

 [1] "IGKV5-2" "NDUFB7"  "LGALS1"  "UBL5"    "S100A6"  "S100A11" "CUTA"    "IFITM3"  "JTB"     "IL17RB" 

program  2 top genes:

 [1] "EGR1"    "C6orf62" "JUNB"    "DNAJA1"  "ATF3"    "IER2"    "ERRFI1"  "KLF6"    "CDKN1A"  "MTHFD2" 

program  3 top genes:

 [1] "RP1-128M12.3"  "RP11-374F3.3"  "RP11-403A21.3" "RP11-454L9.2"  "AC097500.1"    "RP11-463H24.4" "RP11-515O17.2"
 [8] "RP11-536L3.4"  "PALD1"         "AL049758.2"   

program  4 top genes:

 [1] "LTF"           "PIGR"          "FMO2"          "HSD17B2"       "MLPH"          "PRR15L"        "RF00019.219"  
 [8] "RP11-817J15.2" "CD14"          "CLU"          

meta3 = FetchData(object = acc1_cancer_cells,vars = c("metagene.3"))
ggplot(meta3, aes(x=metagene.3)) + 
  geom_density()

meta3[,1] = as.numeric(meta3[,1])
sum(meta3[,1]>0,na.rm = T )

[1] 82

sum(meta3[,1]==0,na.rm = T )

[1] 53

10.4 assignment UMAP

larger_by = 2
message(paste("larger_by = ", larger_by))

larger_by =  2

acc1_cancer_cells = program_assignment(dataset = acc1_cancer_cells,larger_by = larger_by,program_names = colnames(all_metagenes))
selected_k =4
colors =  rainbow(selected_k)
colors = c(colors,"grey")
DimPlot(acc1_cancer_cells,group.by = "program.assignment",pt.size = 2,cols =colors)

show cell cycle program:

hallmark_name = "GO_MITOTIC_CELL_CYCLE"
genesets  =GSEABase::getGmt("./Data/h.all.v7.0.symbols.pluscc.gmt")
var_features=acc1_cancer_cells@assays$RNA@var.features
geneIds= genesets[[hallmark_name]]@geneIds
score <- apply(acc1_cancer_cells@assays$RNA@data[intersect(geneIds,var_features),],2,mean)
acc1_cancer_cells=AddMetaData(acc1_cancer_cells,score,hallmark_name)

FeaturePlot(object = acc1_cancer_cells,features = hallmark_name)

cc_vs_program2 = FetchData(object = acc1_cancer_cells,vars = c("metagene.2",hallmark_name))
cor(cc_vs_program2[1],cc_vs_program2[2])

           GO_MITOTIC_CELL_CYCLE
metagene.2             0.6127281

10.5 Comparisions

cnv_vs_hpv = FetchData(object = acc1_cancer_cells,vars = c("cnv.cluster","hpv33_positive"))
test <- fisher.test(table(cnv_vs_hpv))
ggbarstats(
  cnv_vs_hpv, cnv.cluster, hpv33_positive,
  results.subtitle = FALSE,
  subtitle = paste0(
    "Fisher's exact test", ", p-value = ",
    ifelse(test$p.value < 0.001, "< 0.001", round(test$p.value, 3))
  )
)

cnv_vs_cnmf = FetchData(object = acc1_cancer_cells,vars = c("program.assignment","cnv.cluster"))
cnv_vs_cnmf = cnv_vs_cnmf %>% dplyr::filter(program.assignment == "1" |program.assignment == "2" )
test <- fisher.test(table(cnv_vs_cnmf))
ggbarstats(
  cnv_vs_cnmf, program.assignment, cnv.cluster,
  results.subtitle = FALSE,
  subtitle = paste0(
    "Fisher's exact test", ", p-value = ",
    ifelse(test$p.value < 0.001, "< 0.001", round(test$p.value, 3))
  )
)

cnmf_vs_hpv = FetchData(object = acc1_cancer_cells,vars = c("program.assignment","hpv33_positive"))
cnmf_vs_hpv = cnmf_vs_hpv %>% dplyr::filter(program.assignment == "1" |program.assignment == "2" )
test <- fisher.test(table(cnmf_vs_hpv))
ggbarstats(
  cnmf_vs_hpv, program.assignment, hpv33_positive,
  results.subtitle = FALSE,
  subtitle = paste0(
    "Fisher's exact test", ", p-value = ",
    ifelse(test$p.value < 0.001, "< 0.001", round(test$p.value, 3))
  )
)

myb_vs_cnv = FetchData(object = acc1_cancer_cells,vars = c("cnv.cluster","MYB"))
myb_vs_cnv $cnv.cluster = as.character(myb_vs_cnv $cnv.cluster )

ggboxplot(myb_vs_cnv, x = "cnv.cluster", y = "MYB",
          palette = "jco",
          add = "jitter")+ stat_compare_means(method = "wilcox.test",comparisons = list(c("1","2")))

myb_vs_hpv = FetchData(object = acc1_cancer_cells,vars = c("hpv33_positive","MYB"))
myb_vs_hpv $hpv33_positive = as.character(myb_vs_hpv $hpv33_positive )

ggboxplot(myb_vs_hpv, x = "hpv33_positive", y = "MYB",
          palette = "jco",
          add = "jitter")+ stat_compare_means(method = "wilcox.test",comparisons = list(c("positive","negative")))+ stat_summary(fun.data = function(x) data.frame(y=15, label = paste("Mean=",round(mean(x),digits = 2))), geom="text") +ylab("log2(MYB)")

plate_1_new = subset(acc1_cancer_cells,subset = orig.ident == "ACC.plate2")
myb_vs_hpv = FetchData(object = plate_1_new,vars = c("hpv33_positive","MYB"))
myb_vs_hpv $hpv33_positive = as.character(myb_vs_hpv $hpv33_positive )

# myb_vs_hpv$MYB = 2**myb_vs_hpv$MYB

p = ggboxplot(myb_vs_hpv, x = "hpv33_positive", y = "MYB",
          palette = "jco",
          add = "jitter")+ stat_compare_means(method = "wilcox.test",comparisons = list(c("positive","negative")))+ stat_summary(fun.data = function(x) data.frame(y=15, label = paste("Mean=",round(mean(x),digits = 2))), geom="text") +ylab("log2(MYB)")+ggtitle("ACC.plate2")


plate_2_new = subset(acc1_cancer_cells,subset = orig.ident == "ACC1.P3")
myb_vs_hpv = FetchData(object = plate_2_new,vars = c("hpv33_positive","MYB"))
myb_vs_hpv $hpv33_positive = as.character(myb_vs_hpv $hpv33_positive )
# myb_vs_hpv$MYB = 2**myb_vs_hpv$MYB

p+
ggboxplot(myb_vs_hpv, x = "hpv33_positive", y = "MYB",
          palette = "jco",
          add = "jitter")+ stat_compare_means(method = "wilcox.test",comparisons = list(c("positive","negative")))+ stat_summary(fun.data = function(x) data.frame(y=15, label = paste("Mean=",round(mean(x),digits = 2))), geom="text") +ylab("log2(MYB)")+ggtitle("ACC1.P3")

Warning in wilcox.test.default(c(4.76973518359805, 7.43610462068823, 4.9899976998433,  :
  cannot compute exact p-value with ties

hpvReads_vs_myb = FetchData(object = acc1_cancer_cells,vars = c("HPV33.reads","MYB"))
corr = cor(hpvReads_vs_myb$HPV33.reads,hpvReads_vs_myb$MYB)
print("correlation of MYB abd hpv33_reads:" %>% paste(corr %>% round(digits = 2)))

11 Plate bias

acc1_cancer_cells_data = acc1_cancer_cells@assays[["RNA"]]@data %>% as.data.frame()
acc1_cancer_cells_data = cor(acc1_cancer_cells_data)
annotation = FetchData(object = acc1_cancer_cells,vars = c("orig.ident"))

colors <- c(seq(-1,1,by=0.01))
  my_palette <- c("blue",colorRampPalette(colors = c("blue", "white", "red"))
                                                   (n = length(colors)-3), "red")
  
pht1 = pheatmap(acc1_cor,annotation_col  = annotation,fontsize = 6,breaks = colors, color = my_palette,show_colnames = F,show_rownames = F)

acc1_cancer_cells_data = acc1_cancer_cells@assays[["RNA"]]@data %>% as.data.frame()
acc1_cancer_cells_data = cor(acc1_cancer_cells_data)
annotation = FetchData(object = acc1_cancer_cells,vars = c("cnv.cluster"))

colors <- c(seq(-1,1,by=0.01))
  my_palette <- c("blue",colorRampPalette(colors = c("blue", "white", "red"))
                                                   (n = length(colors)-3), "red")
  
pht1 = pheatmap(acc1_cor,annotation_col  = annotation,fontsize = 6,breaks = colors, color = my_palette,show_colnames = F,show_rownames = F)

cnv_vs_plate = FetchData(object = acc1_cancer_cells,vars = c("cnv.cluster","orig.ident"))
test <- fisher.test(table(cnv_vs_plate))
ggbarstats(
  cnv_vs_plate, cnv.cluster, orig.ident,
  results.subtitle = FALSE,
  subtitle = paste0(
    "Fisher's exact test", ", p-value = ",
    ifelse(test$p.value < 0.001, "< 0.001", round(test$p.value, 3))
  )
)

# creat colours for each group
cnv_vs_plate = FetchData(object = acc1_cancer_cells,vars = c("cnv.cluster","orig.ident","genes_detected"))
cnv_vs_plate$genes_detected[cnv_vs_plate$genes_detected>8000] = 8000
rownames(cnv_vs_plate) = rownames(cnv_vs_plate) %>% gsub(pattern = "_",replacement = "\\.")
cnv_vs_plate$cnv.cluster = as.character(cnv_vs_plate$cnv.cluster)
cnv_vs_plate = cnv_vs_plate %>% dplyr::rename(plate = "orig.ident")
cnv_vs_plate = cnv_vs_plate %>% arrange(plate,genes_detected)
infercnv.observations2_ordered <- infercnv.observations2[match(rownames(cnv_vs_plate), rownames(infercnv.observations2) ),]

annoCol <- list(plate = c(ACC1.P3 = "red",ACC.plate2 = "green"),cnv.cluster =c("1"="green","2" = "red"))

pheatmap(infercnv.observations2_ordered,cluster_cols = F,cluster_rows = F, show_rownames = F,show_colnames = F, breaks = breaks,color = colorRampPalette(rev(c("darkred", "white", "darkblue")))(15),annotation_row = cnv_vs_plate,annotation_colors = annoCol)

# creat colours for each group
cnv_vs_plate = FetchData(object = acc1_cancer_cells,vars = c("cnv.cluster","orig.ident"))

rownames(cnv_vs_plate) = rownames(cnv_vs_plate) %>% gsub(pattern = "_",replacement = "\\.")
cnv_vs_plate$cnv.cluster = as.character(cnv_vs_plate$cnv.cluster)
cnv_vs_plate = cnv_vs_plate %>% dplyr::rename(plate = "orig.ident")
annoCol <- list(plate = c(ACC1.P3 = "red",ACC.plate2 = "green"),cnv.cluster =c("1"="green","2" = "red"))

pheatmap(infercnv.observations2,cluster_cols = F,cluster_rows = F, show_rownames = F,show_colnames = F, breaks = breaks,color = colorRampPalette(rev(c("darkred", "white", "darkblue")))(15),annotation_row = cnv_vs_plate,annotation_colors = annoCol)

data = FetchData(object = acc1_cancer_cells,vars = c("metagene.1","metagene.2"))
# data$genes_detected = min_max_normalize(data$genes_detected)
annotation = FetchData(object = acc1_cancer_cells,vars = c("genes_detected","program.assignment","orig.ident"))
annotation = annotation %>% dplyr::rename(plate = "orig.ident")
annotation$genes_detected[annotation$genes_detected>8000] = 8000
annotation = annotation %>% arrange(plate,genes_detected)
data <- data[match(rownames(annotation), rownames(data) ),]
annoCol <- list(program.assignment = c(metagene.1 = "red",metagene.2 = "grey96",metagene.3 = "yellow",metagene.4 = "blue","NA" = "grey"),plate = c(ACC1.P3 = "red",ACC.plate2 = "green"))

colors <- c(seq(-1,1,by=0.01))
  my_palette <- c("blue",colorRampPalette(colors = c("blue", "white", "red"))
                  (n = length(colors)-3), "red")
  
pheatmap(data,cluster_cols = F,cluster_rows = F,show_rownames = F,color = my_palette, breaks = colors,annotation_row = annotation,annotation_colors = annoCol)

Warning in grSoftVersion() :
  unable to load shared object '/usr/local/lib/R/modules//R_X11.so':
  libXt.so.6: cannot open shared object file: No such file or directory

plate_3 = cnmf_vs_plate %>% dplyr::filter((program.assignment == 1 & orig.ident == "ACC1.P3") ) %>% rownames() 
plate_2 = cnmf_vs_plate %>% dplyr::filter((program.assignment == 2 & orig.ident == "ACC.plate2")) %>% rownames()
cells = list(ACC1.P3 = plate_3,ACC.plate2  = plate_2)

11.1 Results

11.1.1 exceptions

exceptions_plt = DimPlot(object = acc1_cancer_cells, cells.highlight = cells, cols.highlight = c("green","red"), cols = "gray", order = TRUE,pt.size = 2,sizes.highlight = 2,combine = F) 

Warning messages: 1: No Python documentation found for ‘cnmf_obj.prepare’. 2: No Python documentation found for ‘cnmf_obj.prepare’.

colors = c("green","red","cyan","purple","grey")
program.assignment_plt = DimPlot(acc1_cancer_cells,group.by = "program.assignment",pt.size = 2,cols = colors,combine = F)
metagene.1_plt <- FeaturePlot(object = acc1_cancer_cells,features = c("metagene.1"),combine = F)
metagene.2_plt = FeaturePlot(object = acc1_cancer_cells,features = c("metagene.2"),combine = F)

lst = list(exceptions = exceptions_plt, program.assignment = program.assignment_plt,metagene.1 = metagene.1_plt,metagene.2 = metagene.2_plt)

for (i in 1:length(lst)) {
  cat("### ",names(lst)[i]," \n")
  print(
    lst[[i]]
    )
  plot.new()
  dev.off()
  cat(' \n\n')
}

11.1.2 exceptions

[[1]] Warning in grSoftVersion() : unable to load shared object ‘/usr/local/lib/R/modules//R_X11.so’: libXt.so.6: cannot open shared object file: No such file or directory

11.1.3 program.assignment

[[1]]

11.1.4 metagene.1

[[1]]

11.1.5 metagene.2

[[1]]

NA